From 2a874ee9caca6c9b90a4332da506f6563e4cd997 Mon Sep 17 00:00:00 2001 From: Tommaso Verzegnassi Date: Tue, 23 May 2023 22:55:18 +0200 Subject: [PATCH] vedi commit precedente --- indexer/balance_sheet.ipynb | 77 +++++++++++++++++++++---------------- 1 file changed, 43 insertions(+), 34 deletions(-) diff --git a/indexer/balance_sheet.ipynb b/indexer/balance_sheet.ipynb index 3897e07..ee9bfb1 100644 --- a/indexer/balance_sheet.ipynb +++ b/indexer/balance_sheet.ipynb @@ -1,32 +1,26 @@ { "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

Claudio, questo notebook genera la chart relativa alla salute finanziaria. Essenzialmente ci sono quattro colonne che sono stackate a coppie di due: Total Assets/Current Assets e Total Debt/Current Debt. Sostanzialmente asset e debiti a breve termine e lungo termine (quelli a lungo termine saranno sempre superiori in quanto incorporano quelli a breve termine in entrambi i casi). Come vedi il colore degli asset e debiti a breve è molto più marcato perché questi sono nettamente più rilevanti. Se hai qualche idea su come mostrarlo diversamente sono bene accette, io penso che questa soluzione faccia il suo, penso che le stacked bar siano molto efficaci in questo contesto. \n", + "

\n", + "\n", + "

*Nota: ho scelto proprio APPL e MSFT per mostrare che secondo me anche questo garfico ha bisogno di una versione \"relativa\". Quello che vedi sotto compara i valori assoluti, però anche averne uno in cui le altezze delle colonne sono determinate rispetto alle dimensioni dell'azienda non sarebbe male. Domani vediamo comunque.

" + ] + }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 42, "metadata": {}, "outputs": [ - { - "ename": "AttributeError", - "evalue": "This method only works with the ScalarFormatter", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/opt/anaconda3/envs/visual-analytics/lib/python3.11/site-packages/matplotlib/axes/_base.py:3262\u001b[0m, in \u001b[0;36m_AxesBase.ticklabel_format\u001b[0;34m(self, axis, style, scilimits, useOffset, useLocale, useMathText)\u001b[0m\n\u001b[1;32m 3261\u001b[0m \u001b[39mif\u001b[39;00m is_sci_style \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m-> 3262\u001b[0m axis\u001b[39m.\u001b[39;49mmajor\u001b[39m.\u001b[39;49mformatter\u001b[39m.\u001b[39;49mset_scientific(is_sci_style)\n\u001b[1;32m 3263\u001b[0m \u001b[39mif\u001b[39;00m scilimits \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n", - "\u001b[0;31mAttributeError\u001b[0m: 'FuncFormatter' object has no attribute 'set_scientific'", - "\nThe above exception was the direct cause of the following exception:\n", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[22], line 42\u001b[0m\n\u001b[1;32m 39\u001b[0m plt\u001b[39m.\u001b[39mshow()\n\u001b[1;32m 41\u001b[0m ticker_list \u001b[39m=\u001b[39m [\u001b[39m'\u001b[39m\u001b[39mMETA\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39mAAPL\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39mKO\u001b[39m\u001b[39m'\u001b[39m]\n\u001b[0;32m---> 42\u001b[0m compare_balance_sheets(ticker_list)\n", - "Cell \u001b[0;32mIn[22], line 36\u001b[0m, in \u001b[0;36mcompare_balance_sheets\u001b[0;34m(ticker_list)\u001b[0m\n\u001b[1;32m 33\u001b[0m ax\u001b[39m.\u001b[39mgrid(\u001b[39mFalse\u001b[39;00m)\n\u001b[1;32m 35\u001b[0m \u001b[39m# Remove scientific notation\u001b[39;00m\n\u001b[0;32m---> 36\u001b[0m ax\u001b[39m.\u001b[39;49mticklabel_format(style\u001b[39m=\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39mplain\u001b[39;49m\u001b[39m'\u001b[39;49m)\n\u001b[1;32m 38\u001b[0m plt\u001b[39m.\u001b[39mtight_layout() \u001b[39m# Adjust subplot spacing\u001b[39;00m\n\u001b[1;32m 39\u001b[0m plt\u001b[39m.\u001b[39mshow()\n", - "File \u001b[0;32m~/opt/anaconda3/envs/visual-analytics/lib/python3.11/site-packages/matplotlib/axes/_base.py:3272\u001b[0m, in \u001b[0;36m_AxesBase.ticklabel_format\u001b[0;34m(self, axis, style, scilimits, useOffset, useLocale, useMathText)\u001b[0m\n\u001b[1;32m 3270\u001b[0m axis\u001b[39m.\u001b[39mmajor\u001b[39m.\u001b[39mformatter\u001b[39m.\u001b[39mset_useMathText(useMathText)\n\u001b[1;32m 3271\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mAttributeError\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n\u001b[0;32m-> 3272\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mAttributeError\u001b[39;00m(\n\u001b[1;32m 3273\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mThis method only works with the ScalarFormatter\u001b[39m\u001b[39m\"\u001b[39m) \u001b[39mfrom\u001b[39;00m \u001b[39merr\u001b[39;00m\n", - "\u001b[0;31mAttributeError\u001b[0m: This method only works with the ScalarFormatter" - ] - }, { "data": { - "image/png": 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" ] }, "metadata": {}, @@ -44,38 +38,53 @@ "def compare_balance_sheets(ticker_list: list):\n", " num_charts = len(ticker_list)\n", "\n", - " # Create a single figure object and subplots\n", " fig, axes = plt.subplots(1, num_charts, figsize=(12*num_charts, 12), sharey=True)\n", "\n", - " # Set font properties\n", + " \n", " font = font_manager.FontProperties(weight='bold', size=12)\n", "\n", " for i, ticker in enumerate(ticker_list):\n", " assets_debt = pd.read_csv(r'../Companies_Data/'+ticker+'_Data/'+ticker+'_balance_sheet_4Y+4Q.csv')\n", " selected_data = assets_debt[['TotalAssets', 'TotalDebt', 'CurrentAssets', 'CurrentDebt']]\n", "\n", - " sns.set(style=\"whitegrid\") # Set seaborn style\n", + " sns.set(style=\"whitegrid\") \n", "\n", - " # Create bar plot for Total Assets\n", " ax = axes[i]\n", - " ax.bar(range(len(selected_data.columns)), selected_data.iloc[0], color='green')\n", - " ax.set_title(f'{ticker} - Total Assets', fontproperties=font, fontsize=14, weight='bold')\n", + " asset_labels = ['Total Assets & Current Assets', ' Total Debt & Current Debt']\n", + " asset_values = [selected_data.iloc[0]['TotalAssets'], selected_data.iloc[0]['CurrentAssets']]\n", + " current_asset_values = [0, selected_data.iloc[0]['CurrentAssets']]\n", + " ax.bar(range(len(asset_labels)), asset_values, color=['green', 'red'], label='Assets', alpha=0.5)\n", + " ax.bar(range(len(asset_labels)), current_asset_values, color=['white', 'red'], alpha=0.5)\n", + "\n", + " \n", + " debt_labels = ['Total Debt/ Current Debt', '']\n", + " debt_values = [selected_data.iloc[0]['TotalDebt'], selected_data.iloc[0]['CurrentDebt']]\n", + " current_debt_values = [0, selected_data.iloc[0]['CurrentDebt']]\n", + " ax.bar(range(len(debt_labels)), debt_values, color=['darkgreen', 'darkred'], label='Debts')\n", + " ax.bar(range(len(debt_labels)), current_debt_values, color=['white', 'darkred'])\n", + "\n", + " ax.set_title(f'{ticker} - Balance Sheet', fontproperties=font, fontsize=14, weight='bold')\n", " ax.set_xlabel(ticker, fontproperties=font)\n", " ax.set_ylabel('Amount in $', fontproperties=font)\n", - " ax.set_xticks(range(len(selected_data.columns)))\n", - " ax.set_xticklabels(selected_data.columns, fontproperties=font)\n", + " ax.set_xticks(range(len(asset_labels))) \n", + " ax.set_xticklabels(asset_labels, fontproperties=font)\n", "\n", - " # Remove background grid\n", - " ax.grid(False)\n", "\n", - " # Remove scientific notation\n", - " ax.ticklabel_format(style='plain')\n", + " ax.xaxis.grid(False)\n", + " ax.yaxis.grid(False)\n", "\n", - " plt.tight_layout() # Adjust subplot spacing\n", + " \n", + " green_patch = plt.Line2D([0], [0], color='green', alpha=0.5, linewidth=0, marker='s')\n", + " red_patch = plt.Line2D([0], [0], color='red', alpha=0.5, linewidth=0, marker='s')\n", + " ax.legend([green_patch, red_patch], ['Assets', 'Debts'], loc='best')\n", + "\n", + "\n", + " plt.tight_layout() \n", " plt.show()\n", "\n", - "ticker_list = ['META', 'AAPL', 'KO']\n", + "ticker_list = ['AAPL', 'MSFT']\n", "compare_balance_sheets(ticker_list)\n", + "\n", "\n" ] }